Legal AI
A Hebrew-first platform for payslip and legal document analysis using OCR and LLM workflows.
This is a private/internal product currently in development. This case study focuses on architecture, product thinking, and technical direction.
Problem
Legal teams and private clients were processing complex documents manually, which caused slow response times and inconsistent issue detection in payslips.
Constraints
Documents arrived in mixed formats and uneven scan quality, while Hebrew legal language required high extraction precision and clear explanations.
Architecture / Approach
I designed a multi-stage pipeline: file intake, Hebrew-focused OCR, field normalization, and an LLM layer that returns structured summaries with review-ready highlights.
Key decisions
I separated extraction and interpretation layers to improve reliability and to run regression checks for each document family independently.
Outcome / Current status
The product enabled faster, more consistent review of payslips and legal documents, giving teams a clearer decision flow before legal action.
Lessons
In AI products, user trust depends on transparency: showing source evidence and reasoning is as important as raw model accuracy.
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